def run_mapping(self, excluded_seeds):
logger.info('Running prelim_map on %s.', self)
scratch_path = self.get_scratch_path()
with open(self.prelim_csv, 'w') as prelim_csv:
prelim_map(self.trimmed1_fastq,
self.trimmed2_fastq,
prelim_csv,
work_path=scratch_path,
excluded_seeds=excluded_seeds)
logger.info('Running remap on %s.', self)
if self.debug_remap:
debug_file_prefix = os.path.join(scratch_path, 'debug')
else:
debug_file_prefix = None
with open(self.prelim_csv) as prelim_csv, \
open(self.remap_csv, 'w') as remap_csv, \
open(self.remap_counts_csv, 'w') as counts_csv, \
open(self.remap_conseq_csv, 'w') as conseq_csv, \
open(self.unmapped1_fastq, 'w') as unmapped1, \
open(self.unmapped2_fastq, 'w') as unmapped2:
remap(self.trimmed1_fastq,
self.trimmed2_fastq,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
scratch_path,
debug_file_prefix=debug_file_prefix)
def remap70(fastq1_filename, do_counts=False):
workdir = os.path.dirname(fastq1_filename)
fastq2_filename = get_reverse_filename(fastq1_filename)
prelim_filename = os.path.join(workdir, 'temp70.prelim.csv')
remap_filename = os.path.join(workdir, 'temp70.remap.csv')
remap_counts_filename = os.path.join(workdir, 'temp70.remap_counts.csv')
aligned_filename = os.path.join(workdir, 'temp70.aligned.csv')
nuc_filename = os.path.join(workdir, 'temp70.nuc.csv')
amino_filename = os.path.join(workdir, 'temp70.amino.csv')
failed_align_filename = os.path.join(workdir, 'temp70.failed_align.csv')
with open(prelim_filename, 'w+') as prelim_csv, \
open(remap_filename, 'w+') as remap_csv, \
open(remap_counts_filename, 'w+') as remap_counts_csv, \
open(aligned_filename, 'w+') as aligned_csv, \
open(nuc_filename, 'w+') as nuc_csv, \
open(amino_filename, 'w+') as amino_csv, \
open(failed_align_filename, 'w+') as failed_align_csv, \
open(os.devnull, 'w+') as real_devnull:
devnull = DevNullWrapper(real_devnull)
prelim_map(fastq1_filename, fastq2_filename, prelim_csv)
prelim_csv.seek(0)
remap(fastq1_filename, fastq2_filename, prelim_csv, remap_csv,
remap_counts_csv, devnull, devnull, devnull)
if not do_counts:
remap_counts_csv.close()
return get_max_mapped_counts(remap_counts_filename)
remap_csv.seek(0)
sam2aln(remap_csv, aligned_csv, devnull, failed_align_csv)
aligned_csv.seek(0)
aln2counts(aligned_csv, nuc_csv, amino_csv, devnull, devnull, devnull,
devnull)
return get_gap_level(amino_filename, 'E2', 14)
def remap70(fastq1_filename, do_counts=False):
workdir = os.path.dirname(fastq1_filename)
fastq2_filename = get_reverse_filename(fastq1_filename)
prelim_filename = os.path.join(workdir, 'temp70.prelim.csv')
remap_filename = os.path.join(workdir, 'temp70.remap.csv')
remap_counts_filename = os.path.join(workdir, 'temp70.remap_counts.csv')
aligned_filename = os.path.join(workdir, 'temp70.aligned.csv')
nuc_filename = os.path.join(workdir, 'temp70.nuc.csv')
amino_filename = os.path.join(workdir, 'temp70.amino.csv')
failed_align_filename = os.path.join(workdir, 'temp70.failed_align.csv')
with open(prelim_filename, 'w+') as prelim_csv, \
open(remap_filename, 'w+') as remap_csv, \
open(remap_counts_filename, 'w+') as remap_counts_csv, \
open(aligned_filename, 'w+') as aligned_csv, \
open(nuc_filename, 'w+') as nuc_csv, \
open(amino_filename, 'w+') as amino_csv, \
open(failed_align_filename, 'w+') as failed_align_csv, \
open(os.devnull, 'w+') as real_devnull:
devnull = DevNullWrapper(real_devnull)
prelim_map(fastq1_filename, fastq2_filename, prelim_csv)
prelim_csv.seek(0)
remap(fastq1_filename,
fastq2_filename,
prelim_csv,
remap_csv,
remap_counts_csv,
devnull,
devnull,
devnull)
if not do_counts:
remap_counts_csv.close()
return get_max_mapped_counts(remap_counts_filename)
remap_csv.seek(0)
sam2aln(remap_csv, aligned_csv, devnull, failed_align_csv)
aligned_csv.seek(0)
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
devnull,
devnull,
devnull,
devnull)
return get_gap_level(amino_filename, 'E2', 14)
def _test(self, read_indexes, debug_file_prefix=None):
read_indexes = reversed(read_indexes)
simple_filename1 = self.filename1 + '_simple.fastq'
self.write_simple_fastq(simple_filename1, read_indexes)
workdir = os.path.dirname(self.filename1)
os.chdir(workdir)
simple_filename2 = get_reverse_filename(simple_filename1)
censored_filename1 = os.path.join(workdir, 'rerun.censored1.fastq')
censored_filename2 = os.path.join(workdir, 'rerun.censored2.fastq')
trimmed_filename1 = os.path.join(workdir, 'rerun.trimmed1.fastq')
trimmed_filename2 = os.path.join(workdir, 'rerun.trimmed2.fastq')
prelim_censored_filename = os.path.join(workdir, 'rerun_censored.prelim.csv')
prelim_trimmed_filename = os.path.join(workdir, 'rerun_trimmed.prelim.csv')
with open(self.bad_cycles_filename, 'rU') as bad_cycles:
bad_cycles = list(csv.DictReader(bad_cycles))
with open(simple_filename1, 'rU') as simple1, \
open(censored_filename1, 'w') as censored1:
censor(simple1, bad_cycles, censored1, use_gzip=False)
with open(simple_filename2, 'rU') as simple2, \
open(censored_filename2, 'w') as censored2:
censor(simple2, bad_cycles, censored2, use_gzip=False)
with open(prelim_censored_filename, 'w+') as prelim_censored_csv, \
open(prelim_trimmed_filename, 'w+') as prelim_trimmed_csv:
prelim_map(censored_filename1,
censored_filename2,
prelim_censored_csv,
nthreads=BOWTIE_THREADS)
trim((simple_filename1, simple_filename2),
self.bad_cycles_filename,
(trimmed_filename1, trimmed_filename2),
use_gzip=False)
prelim_map(trimmed_filename1,
trimmed_filename2,
prelim_trimmed_csv,
nthreads=BOWTIE_THREADS)
prelim_censored_csv.seek(0)
censored_map_count = self.count_mapped(prelim_censored_csv)
prelim_trimmed_csv.seek(0)
trimmed_map_count = self.count_mapped(prelim_trimmed_csv)
return self.get_result(censored_map_count, trimmed_map_count)
def _test(self, read_indexes, debug_file_prefix=None):
read_indexes = reversed(read_indexes)
simple_filename1 = self.filename1 + '_simple.fastq'
self.write_simple_fastq(simple_filename1, read_indexes)
workdir = os.path.dirname(self.filename1)
os.chdir(workdir)
simple_filename2 = get_reverse_filename(simple_filename1)
censored_filename1 = os.path.join(workdir, 'rerun.censored1.fastq')
censored_filename2 = os.path.join(workdir, 'rerun.censored2.fastq')
trimmed_filename1 = os.path.join(workdir, 'rerun.trimmed1.fastq')
trimmed_filename2 = os.path.join(workdir, 'rerun.trimmed2.fastq')
prelim_censored_filename = os.path.join(workdir, 'rerun_censored.prelim.csv')
prelim_trimmed_filename = os.path.join(workdir, 'rerun_trimmed.prelim.csv')
with open(self.bad_cycles_filename, 'rU') as bad_cycles:
bad_cycles = list(csv.DictReader(bad_cycles))
with open(simple_filename1, 'rU') as simple1, \
open(censored_filename1, 'w') as censored1:
censor(simple1, bad_cycles, censored1, use_gzip=False)
with open(simple_filename2, 'rU') as simple2, \
open(censored_filename2, 'w') as censored2:
censor(simple2, bad_cycles, censored2, use_gzip=False)
with open(prelim_censored_filename, 'w+') as prelim_censored_csv, \
open(prelim_trimmed_filename, 'w+') as prelim_trimmed_csv:
prelim_map(censored_filename1,
censored_filename2,
prelim_censored_csv,
nthreads=BOWTIE_THREADS)
trim((simple_filename1, simple_filename2),
self.bad_cycles_filename,
(trimmed_filename1, trimmed_filename2),
use_gzip=False)
prelim_map(trimmed_filename1,
trimmed_filename2,
prelim_trimmed_csv,
nthreads=BOWTIE_THREADS)
prelim_censored_csv.seek(0)
censored_map_count = self.count_mapped(prelim_censored_csv)
prelim_trimmed_csv.seek(0)
trimmed_map_count = self.count_mapped(prelim_trimmed_csv)
return self.get_result(censored_map_count, trimmed_map_count)
def filter_fastqs(self, filename1):
filter_name1 = filename1 + '_filter.fastq'
if os.path.exists(filter_name1):
logging.info('Already filtered.')
return filter_name1
filename2 = get_reverse_filename(filename1)
filter_name2 = filename2 + '_filter.fastq'
workdir = os.path.dirname(filename1)
prelim_filename = os.path.join(workdir, 'filter.prelim.csv')
remap_filename = os.path.join(workdir, 'filter.remap.csv')
with open(prelim_filename, 'w+') as prelim_csv, \
open(remap_filename, 'w') as remap_csv, \
open(os.devnull, 'w+') as real_devnull:
print('Preliminary mapping to filter.')
devnull = DevNullWrapper(real_devnull)
prelim_map(filename1,
filename2,
prelim_csv,
nthreads=BOWTIE_THREADS)
prelim_csv.seek(0)
print('Remapping to filter.')
remap(filename1,
filename2,
prelim_csv,
remap_csv,
devnull,
devnull,
devnull,
devnull,
nthreads=BOWTIE_THREADS)
with open(remap_filename, 'rU') as remap_csv:
print('Filtering.')
reader = DictReader(remap_csv)
mapped_qnames = {row['qname']
for row in reader
if row['rname'].startswith('HCV-1') and 2500 < int(row['pos']) < 3500}
self.filter_reads(filename1, filter_name1, mapped_qnames)
self.filter_reads(filename2, filter_name2, mapped_qnames)
logging.info('Finished filtering with %d reads.', len(mapped_qnames))
return filter_name1
def filter_fastqs(self, filename1):
filter_name1 = filename1 + '_filter.fastq'
if os.path.exists(filter_name1):
logging.info('Already filtered.')
return filter_name1
filename2 = get_reverse_filename(filename1)
filter_name2 = filename2 + '_filter.fastq'
workdir = os.path.dirname(filename1)
prelim_filename = os.path.join(workdir, 'filter.prelim.csv')
remap_filename = os.path.join(workdir, 'filter.remap.csv')
with open(prelim_filename, 'w+') as prelim_csv, \
open(remap_filename, 'w') as remap_csv, \
open(os.devnull, 'w+') as real_devnull:
print('Preliminary mapping to filter.')
devnull = DevNullWrapper(real_devnull)
prelim_map(filename1,
filename2,
prelim_csv,
nthreads=BOWTIE_THREADS)
prelim_csv.seek(0)
print('Remapping to filter.')
remap(filename1,
filename2,
prelim_csv,
remap_csv,
devnull,
devnull,
devnull,
devnull,
nthreads=BOWTIE_THREADS)
with open(remap_filename, 'rU') as remap_csv:
print('Filtering.')
reader = DictReader(remap_csv)
mapped_qnames = {row['qname']
for row in reader
if row['rname'].startswith('HCV-1') and 2500 < int(row['pos']) < 3500}
self.filter_reads(filename1, filter_name1, mapped_qnames)
self.filter_reads(filename2, filter_name2, mapped_qnames)
logging.info('Finished filtering with %d reads.', len(mapped_qnames))
return filter_name1
def process_sample(sample_index, run_info, data_path, pssm):
""" Process a single sample.
:param sample_index: which sample to process from the session JSON
:param run_info: run parameters loaded from the session JSON
:param str data_path: the root folder for all BaseSpace data
:param pssm: the pssm library for running G2P analysis
"""
scratch_path = os.path.join(data_path, 'scratch')
sample_info = run_info.samples[sample_index]
sample_id = sample_info['Id']
sample_name = sample_info['Name']
sample_dir = os.path.join(data_path, 'input', 'samples', sample_id, 'Data',
'Intensities', 'BaseCalls')
if not os.path.exists(sample_dir):
sample_dir = os.path.join(data_path, 'input', 'samples', sample_id)
sample_path = None
for root, _dirs, files in os.walk(sample_dir):
sample_paths = fnmatch.filter(files, '*_R1_*')
if sample_paths:
sample_path = os.path.join(root, sample_paths[0])
break
if sample_path is None:
raise RuntimeError(
'No R1 file found for sample id {}.'.format(sample_id))
sample_path2 = sample_path.replace('_R1_', '_R2_')
if not os.path.exists(sample_path2):
raise RuntimeError('R2 file missing for sample id {}: {!r}.'.format(
sample_id, sample_path2))
logger.info('Processing sample %s (%d of %d): %s (%s).', sample_id,
sample_index + 1, len(run_info.samples), sample_name,
sample_path)
sample_out_path = create_app_result(data_path,
run_info,
sample_info,
description='Mapping results',
suffix='_QC')
sample_scratch_path = os.path.join(scratch_path, sample_name)
makedirs(sample_scratch_path)
censored_path1 = os.path.join(sample_scratch_path, 'censored1.fastq')
read_summary_path1 = os.path.join(sample_scratch_path, 'read1_summary.csv')
censor_sample(sample_path, os.path.join(scratch_path, 'bad_cycles.csv'),
censored_path1, read_summary_path1)
censored_path2 = os.path.join(sample_scratch_path, 'censored2.fastq')
read_summary_path2 = os.path.join(sample_scratch_path, 'read2_summary.csv')
censor_sample(sample_path2, os.path.join(scratch_path, 'bad_cycles.csv'),
censored_path2, read_summary_path2)
logger.info('Running prelim_map (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'prelim.csv'),
'wb') as prelim_csv:
prelim_map(censored_path1, censored_path2, prelim_csv)
logger.info('Running remap (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'prelim.csv'), 'rU') as prelim_csv, \
open(os.path.join(sample_scratch_path, 'remap.csv'), 'wb') as remap_csv, \
open(os.path.join(sample_out_path, 'remap_counts.csv'), 'wb') as counts_csv, \
open(os.path.join(sample_out_path, 'remap_conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(sample_out_path, 'unmapped1.fastq'), 'w') as unmapped1, \
open(os.path.join(sample_out_path, 'unmapped2.fastq'), 'w') as unmapped2:
remap(censored_path1,
censored_path2,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
sample_scratch_path,
nthreads=1)
logger.info('Running sam2aln (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'remap.csv'), 'rU') as remap_csv, \
open(os.path.join(sample_scratch_path, 'aligned.csv'), 'wb') as aligned_csv, \
open(os.path.join(sample_out_path, 'conseq_ins.csv'), 'wb') as insert_csv, \
open(os.path.join(sample_out_path, 'failed_read.csv'), 'wb') as failed_csv:
sam2aln(remap_csv, aligned_csv, insert_csv, failed_csv)
logger.info('Running aln2counts (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'aligned.csv'), 'rU') as aligned_csv, \
open(os.path.join(sample_out_path, 'nuc.csv'), 'wb') as nuc_csv, \
open(os.path.join(sample_out_path, 'amino.csv'), 'wb') as amino_csv, \
open(os.path.join(sample_out_path, 'coord_ins.csv'), 'wb') as coord_ins_csv, \
open(os.path.join(sample_out_path, 'conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(sample_out_path, 'failed_align.csv'), 'wb') as failed_align_csv, \
open(os.path.join(sample_out_path, 'nuc_variants.csv'), 'wb') as nuc_variants_csv, \
open(os.path.join(sample_scratch_path, 'coverage_summary.csv'), 'wb') as coverage_summary_csv:
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
nuc_variants_csv,
coverage_summary_csv=coverage_summary_csv)
logger.info('Running coverage_plots (%d of %d).', sample_index + 1,
len(run_info.samples))
coverage_path = os.path.join(sample_out_path, 'coverage')
with open(os.path.join(sample_out_path, 'amino.csv'), 'rU') as amino_csv, \
open(os.path.join(sample_out_path, 'coverage_scores.csv'), 'w') as coverage_scores_csv:
coverage_plot(amino_csv,
coverage_scores_csv,
path_prefix=coverage_path)
with open(os.path.join(sample_out_path, 'coverage_scores.csv'),
'rU') as coverage_scores_csv:
reader = csv.DictReader(coverage_scores_csv)
is_v3loop_good = False
for row in reader:
if row['region'] == 'V3LOOP':
is_v3loop_good = row['on.score'] == '4'
break
if is_v3loop_good:
logger.info('Running sam_g2p (%d of %d).', sample_index + 1,
len(run_info.samples))
g2p_path = create_app_result(data_path,
run_info,
sample_info,
description='Geno To Pheno results',
suffix='_G2P')
with open(os.path.join(sample_scratch_path, 'remap.csv'), 'rU') as remap_csv, \
open(os.path.join(sample_out_path, 'nuc.csv'), 'rU') as nuc_csv, \
open(os.path.join(g2p_path, 'g2p.csv'), 'wb') as g2p_csv, \
open(os.path.join(g2p_path, 'g2p_summary.csv'), 'wb') as g2p_summary_csv:
sam_g2p(pssm=pssm,
remap_csv=remap_csv,
nuc_csv=nuc_csv,
g2p_csv=g2p_csv,
g2p_summary_csv=g2p_summary_csv,
min_count=DEFAULT_MIN_COUNT)
def process_sample(sample_index, run_info, data_path, pssm):
""" Process a single sample.
:param sample_index: which sample to process from the session JSON
:param run_info: run parameters loaded from the session JSON
:param str data_path: the root folder for all BaseSpace data
:param pssm: the pssm library for running G2P analysis
"""
scratch_path = os.path.join(data_path, 'scratch')
sample_info = run_info.samples[sample_index]
sample_id = sample_info['Id']
sample_name = sample_info['Name']
sample_dir = os.path.join(data_path,
'input',
'samples',
sample_id,
'Data',
'Intensities',
'BaseCalls')
if not os.path.exists(sample_dir):
sample_dir = os.path.join(data_path,
'input',
'samples',
sample_id)
sample_path = None
for root, _dirs, files in os.walk(sample_dir):
sample_paths = fnmatch.filter(files, '*_R1_*')
if sample_paths:
sample_path = os.path.join(root, sample_paths[0])
break
if sample_path is None:
raise RuntimeError('No R1 file found for sample id {}.'.format(sample_id))
sample_path2 = sample_path.replace('_R1_', '_R2_')
if not os.path.exists(sample_path2):
raise RuntimeError('R2 file missing for sample id {}: {!r}.'.format(
sample_id,
sample_path2))
logger.info('Processing sample %s (%d of %d): %s (%s).',
sample_id,
sample_index+1,
len(run_info.samples),
sample_name,
sample_path)
sample_out_path = create_app_result(data_path,
run_info,
sample_info,
description='Mapping results',
suffix='_QC')
sample_scratch_path = os.path.join(scratch_path, sample_name)
makedirs(sample_scratch_path)
censored_path1 = os.path.join(sample_scratch_path, 'censored1.fastq')
read_summary_path1 = os.path.join(sample_scratch_path, 'read1_summary.csv')
censor_sample(sample_path,
os.path.join(scratch_path, 'bad_cycles.csv'),
censored_path1,
read_summary_path1)
censored_path2 = os.path.join(sample_scratch_path, 'censored2.fastq')
read_summary_path2 = os.path.join(sample_scratch_path, 'read2_summary.csv')
censor_sample(sample_path2,
os.path.join(scratch_path, 'bad_cycles.csv'),
censored_path2,
read_summary_path2)
logger.info('Running prelim_map (%d of %d).', sample_index+1, len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'prelim.csv'), 'wb') as prelim_csv:
prelim_map(censored_path1,
censored_path2,
prelim_csv)
logger.info('Running remap (%d of %d).', sample_index+1, len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'prelim.csv'), 'rU') as prelim_csv, \
open(os.path.join(sample_scratch_path, 'remap.csv'), 'wb') as remap_csv, \
open(os.path.join(sample_out_path, 'remap_counts.csv'), 'wb') as counts_csv, \
open(os.path.join(sample_out_path, 'remap_conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(sample_out_path, 'unmapped1.fastq'), 'w') as unmapped1, \
open(os.path.join(sample_out_path, 'unmapped2.fastq'), 'w') as unmapped2:
remap(censored_path1,
censored_path2,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
sample_scratch_path,
nthreads=1)
logger.info('Running sam2aln (%d of %d).', sample_index+1, len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'remap.csv'), 'rU') as remap_csv, \
open(os.path.join(sample_scratch_path, 'aligned.csv'), 'wb') as aligned_csv, \
open(os.path.join(sample_out_path, 'conseq_ins.csv'), 'wb') as insert_csv, \
open(os.path.join(sample_out_path, 'failed_read.csv'), 'wb') as failed_csv:
sam2aln(remap_csv, aligned_csv, insert_csv, failed_csv)
logger.info('Running aln2counts (%d of %d).', sample_index+1, len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'aligned.csv'), 'rU') as aligned_csv, \
open(os.path.join(sample_out_path, 'nuc.csv'), 'wb') as nuc_csv, \
open(os.path.join(sample_out_path, 'amino.csv'), 'wb') as amino_csv, \
open(os.path.join(sample_out_path, 'coord_ins.csv'), 'wb') as coord_ins_csv, \
open(os.path.join(sample_out_path, 'conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(sample_out_path, 'failed_align.csv'), 'wb') as failed_align_csv, \
open(os.path.join(sample_out_path, 'nuc_variants.csv'), 'wb') as nuc_variants_csv, \
open(os.path.join(sample_scratch_path, 'coverage_summary.csv'), 'wb') as coverage_summary_csv:
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
nuc_variants_csv,
coverage_summary_csv=coverage_summary_csv)
logger.info('Running coverage_plots (%d of %d).', sample_index+1, len(run_info.samples))
coverage_path = os.path.join(sample_out_path, 'coverage')
with open(os.path.join(sample_out_path, 'amino.csv'), 'rU') as amino_csv, \
open(os.path.join(sample_out_path, 'coverage_scores.csv'), 'w') as coverage_scores_csv:
coverage_plot(amino_csv, coverage_scores_csv, path_prefix=coverage_path)
with open(os.path.join(sample_out_path, 'coverage_scores.csv'), 'rU') as coverage_scores_csv:
reader = csv.DictReader(coverage_scores_csv)
is_v3loop_good = False
for row in reader:
if row['region'] == 'V3LOOP':
is_v3loop_good = row['on.score'] == '4'
break
if is_v3loop_good:
logger.info('Running sam_g2p (%d of %d).', sample_index+1, len(run_info.samples))
g2p_path = create_app_result(data_path,
run_info,
sample_info,
description='Geno To Pheno results',
suffix='_G2P')
with open(os.path.join(sample_scratch_path, 'remap.csv'), 'rU') as remap_csv, \
open(os.path.join(sample_out_path, 'nuc.csv'), 'rU') as nuc_csv, \
open(os.path.join(g2p_path, 'g2p.csv'), 'wb') as g2p_csv, \
open(os.path.join(g2p_path, 'g2p_summary.csv'), 'wb') as g2p_summary_csv:
sam_g2p(pssm=pssm,
remap_csv=remap_csv,
nuc_csv=nuc_csv,
g2p_csv=g2p_csv,
g2p_summary_csv=g2p_summary_csv,
min_count=DEFAULT_MIN_COUNT)
def process_sample(self, fastq1, progress, prefixes, image_paths,
error_log):
fastq2 = fastq1.replace('_R1_001',
'_R2_001').replace('censored1', 'censored2')
if not os.path.exists(fastq2):
raise IOError('ERROR: Missing R2 file for {}'.format(fastq1))
prefix = os.path.basename(fastq1).replace('_L001_R1_001.fastq',
'').replace(
'.censored1.fastq', '')
prefixes.append(prefix)
output_csv = prefix + '.prelim.csv'
self.write('Processing sample {} ({})\n'.format(prefix, progress))
with open(output_csv, 'wb') as handle:
prelim_map(fastq1,
fastq2,
handle,
nthreads=self.nthreads,
callback=self.callback,
stderr=error_log)
# prepare file handles for remap stage
with open(output_csv, 'rU') as prelim_csv, \
open(os.path.join(self.workdir, prefix + '.remap.csv'), 'wb') as remap_csv, \
open(os.path.join(self.workdir, prefix + '.remap_counts.csv'), 'wb') as counts_csv, \
open(os.path.join(self.workdir, prefix + '.remap_conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(self.workdir, prefix + '.unmapped1.fastq'), 'w') as unmapped1, \
open(os.path.join(self.workdir, prefix + '.unmapped2.fastq'), 'w') as unmapped2:
self.write('... remapping\n')
self.parent.update()
self.progress_bar['value'] = 0
remap(fastq1,
fastq2,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
self.workdir,
nthreads=self.nthreads,
callback=self.callback,
stderr=error_log)
# prepare file handles for conversion from SAM format to alignment
with open(os.path.join(self.workdir, prefix + '.remap.csv'), 'rU') as remap_csv, \
open(os.path.join(self.workdir, prefix + '.aligned.csv'), 'wb') as aligned_csv, \
open(os.path.join(self.workdir, prefix + '.insert.csv'), 'wb') as insert_csv, \
open(os.path.join(self.workdir, prefix + '.failed.csv'), 'wb') as failed_csv:
self.write('... converting into alignment\n')
self.parent.update()
sam2aln(remap_csv,
aligned_csv,
insert_csv,
failed_csv,
nthreads=self.nthreads)
with open(os.path.join(self.workdir, prefix + '.aligned.csv'), 'rU') as aligned_csv, \
open(os.path.join(self.workdir, prefix + '.nuc.csv'), 'wb') as nuc_csv, \
open(os.path.join(self.workdir, prefix + '.amino.csv'), 'wb') as amino_csv, \
open(os.path.join(self.workdir, prefix + '.coord_ins.csv'), 'wb') as coord_ins_csv, \
open(os.path.join(self.workdir, prefix + '.conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(self.workdir, prefix + '.failed_align.csv'), 'wb') as failed_align_csv, \
open(os.path.join(self.workdir, prefix + '.nuc_variants.csv'), 'wb') as nuc_variants_csv:
self.parent.update()
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
nuc_variants_csv,
callback=self.callback)
self.write('... generating coverage plots\n')
self.parent.update()
with open(os.path.join(self.workdir, prefix + '.amino.csv'),
'rU') as amino_csv:
image_paths += coverage_plot(amino_csv)
self.write('... performing g2p scoring on samples covering HIV-1 V3\n')
self.parent.update()
with open(os.path.join(self.workdir, prefix + '.remap.csv'), 'rU') as remap_csv, \
open(os.path.join(self.workdir, prefix + '.nuc.csv'), 'rU') as nuc_csv, \
open(os.path.join(self.workdir, prefix + '.g2p.csv'), 'wb') as g2p_csv:
sam_g2p(pssm=self.pssm,
remap_csv=remap_csv,
nuc_csv=nuc_csv,
g2p_csv=g2p_csv)
def process_sample(self, fastq1, progress, prefixes, image_paths, error_log):
fastq2 = fastq1.replace('_R1_001', '_R2_001').replace('censored1',
'censored2')
if not os.path.exists(fastq2):
raise IOError('ERROR: Missing R2 file for {}'.format(fastq1))
prefix = os.path.basename(fastq1).replace('_L001_R1_001.fastq',
'').replace('.censored1.fastq',
'')
prefixes.append(prefix)
output_csv = prefix + '.prelim.csv'
self.write('Processing sample {} ({})\n'.format(prefix, progress))
with open(output_csv, 'wb') as handle:
prelim_map(fastq1,
fastq2,
handle,
nthreads=self.nthreads,
callback=self.callback,
stderr=error_log)
# prepare file handles for remap stage
with open(output_csv, 'rU') as prelim_csv, \
open(os.path.join(self.workdir, prefix + '.remap.csv'), 'wb') as remap_csv, \
open(os.path.join(self.workdir, prefix + '.remap_counts.csv'), 'wb') as counts_csv, \
open(os.path.join(self.workdir, prefix + '.remap_conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(self.workdir, prefix + '.unmapped1.fastq'), 'w') as unmapped1, \
open(os.path.join(self.workdir, prefix + '.unmapped2.fastq'), 'w') as unmapped2:
self.write('... remapping\n')
self.parent.update()
self.progress_bar['value'] = 0
remap(fastq1,
fastq2,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
self.workdir,
nthreads=self.nthreads,
callback=self.callback,
stderr=error_log)
# prepare file handles for conversion from SAM format to alignment
with open(os.path.join(self.workdir, prefix + '.remap.csv'), 'rU') as remap_csv, \
open(os.path.join(self.workdir, prefix + '.aligned.csv'), 'wb') as aligned_csv, \
open(os.path.join(self.workdir, prefix + '.insert.csv'), 'wb') as insert_csv, \
open(os.path.join(self.workdir, prefix + '.failed.csv'), 'wb') as failed_csv:
self.write('... converting into alignment\n')
self.parent.update()
sam2aln(remap_csv,
aligned_csv,
insert_csv,
failed_csv,
nthreads=self.nthreads)
with open(os.path.join(self.workdir, prefix + '.aligned.csv'), 'rU') as aligned_csv, \
open(os.path.join(self.workdir, prefix + '.nuc.csv'), 'wb') as nuc_csv, \
open(os.path.join(self.workdir, prefix + '.amino.csv'), 'wb') as amino_csv, \
open(os.path.join(self.workdir, prefix + '.coord_ins.csv'), 'wb') as coord_ins_csv, \
open(os.path.join(self.workdir, prefix + '.conseq.csv'), 'wb') as conseq_csv, \
open(os.path.join(self.workdir, prefix + '.failed_align.csv'), 'wb') as failed_align_csv, \
open(os.path.join(self.workdir, prefix + '.nuc_variants.csv'), 'wb') as nuc_variants_csv:
self.parent.update()
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
nuc_variants_csv,
callback=self.callback)
self.write('... generating coverage plots\n')
self.parent.update()
with open(os.path.join(self.workdir, prefix + '.amino.csv'), 'rU') as amino_csv:
image_paths += coverage_plot(amino_csv)
self.write('... performing g2p scoring on samples covering HIV-1 V3\n')
self.parent.update()
with open(os.path.join(self.workdir, prefix + '.remap.csv'), 'rU') as remap_csv, \
open(os.path.join(self.workdir, prefix + '.nuc.csv'), 'rU') as nuc_csv, \
open(os.path.join(self.workdir, prefix + '.g2p.csv'), 'wb') as g2p_csv:
sam_g2p(pssm=self.pssm,
remap_csv=remap_csv,
nuc_csv=nuc_csv,
g2p_csv=g2p_csv)
def process(self,
pssm,
excluded_seeds=(),
excluded_projects=(),
force_gzip=False):
""" Process a single sample.
:param pssm: the pssm library for running G2P analysis
:param excluded_seeds: seeds to exclude from mapping
:param excluded_projects: project codes to exclude from reporting
:param bool force_gzip: treat FASTQ files as gzipped, even when they
don't end in .gz
"""
logger.info('Processing %s (%r).', self, self.fastq1)
scratch_path = os.path.dirname(self.prelim_csv)
os.mkdir(scratch_path)
use_gzip = force_gzip or self.fastq1.endswith('.gz')
with open(self.read_summary_csv, 'w') as read_summary:
trim((self.fastq1, self.fastq2),
self.bad_cycles_csv,
(self.trimmed1_fastq, self.trimmed2_fastq),
summary_file=read_summary,
use_gzip=use_gzip)
logger.info('Running fastq_g2p on %s.', self)
with open(self.trimmed1_fastq) as fastq1, \
open(self.trimmed2_fastq) as fastq2, \
open(self.g2p_csv, 'w') as g2p_csv, \
open(self.g2p_summary_csv, 'w') as g2p_summary_csv, \
open(self.g2p_unmapped1_fastq, 'w') as g2p_unmapped1, \
open(self.g2p_unmapped2_fastq, 'w') as g2p_unmapped2, \
open(self.g2p_aligned_csv, 'w') as g2p_aligned_csv:
fastq_g2p(pssm=pssm,
fastq1=fastq1,
fastq2=fastq2,
g2p_csv=g2p_csv,
g2p_summary_csv=g2p_summary_csv,
unmapped1=g2p_unmapped1,
unmapped2=g2p_unmapped2,
aligned_csv=g2p_aligned_csv,
min_count=DEFAULT_MIN_COUNT,
min_valid=MIN_VALID,
min_valid_percent=MIN_VALID_PERCENT)
logger.info('Running prelim_map on %s.', self)
with open(self.prelim_csv, 'w') as prelim_csv:
prelim_map(self.g2p_unmapped1_fastq,
self.g2p_unmapped2_fastq,
prelim_csv,
work_path=scratch_path,
excluded_seeds=excluded_seeds)
logger.info('Running remap on %s.', self)
if self.debug_remap:
debug_file_prefix = os.path.join(scratch_path, 'debug')
else:
debug_file_prefix = None
with open(self.prelim_csv) as prelim_csv, \
open(self.remap_csv, 'w') as remap_csv, \
open(self.remap_counts_csv, 'w') as counts_csv, \
open(self.remap_conseq_csv, 'w') as conseq_csv, \
open(self.unmapped1_fastq, 'w') as unmapped1, \
open(self.unmapped2_fastq, 'w') as unmapped2:
remap(self.g2p_unmapped1_fastq,
self.g2p_unmapped2_fastq,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
scratch_path,
debug_file_prefix=debug_file_prefix)
logger.info('Running sam2aln on %s.', self)
with open(self.remap_csv) as remap_csv, \
open(self.aligned_csv, 'w') as aligned_csv, \
open(self.conseq_ins_csv, 'w') as conseq_ins_csv, \
open(self.failed_csv, 'w') as failed_csv, \
open(self.clipping_csv, 'w') as clipping_csv:
sam2aln(remap_csv,
aligned_csv,
conseq_ins_csv,
failed_csv,
clipping_csv=clipping_csv)
logger.info('Running aln2counts on %s.', self)
with open(self.aligned_csv) as aligned_csv, \
open(self.g2p_aligned_csv) as g2p_aligned_csv, \
open(self.clipping_csv) as clipping_csv, \
open(self.conseq_ins_csv) as conseq_ins_csv, \
open(self.remap_conseq_csv) as remap_conseq_csv, \
open(self.nuc_csv, 'w') as nuc_csv, \
open(self.amino_csv, 'w') as amino_csv, \
open(self.coord_ins_csv, 'w') as coord_ins_csv, \
open(self.conseq_csv, 'w') as conseq_csv, \
open(self.conseq_region_csv, 'w') as conseq_region_csv, \
open(self.failed_align_csv, 'w') as failed_align_csv, \
open(self.coverage_summary_csv, 'w') as coverage_summary_csv:
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
coverage_summary_csv=coverage_summary_csv,
clipping_csv=clipping_csv,
conseq_ins_csv=conseq_ins_csv,
g2p_aligned_csv=g2p_aligned_csv,
remap_conseq_csv=remap_conseq_csv,
conseq_region_csv=conseq_region_csv)
logger.info('Running coverage_plots on %s.', self)
os.makedirs(self.coverage_maps)
with open(self.amino_csv) as amino_csv, \
open(self.coverage_scores_csv, 'w') as coverage_scores_csv:
coverage_plot(amino_csv,
coverage_scores_csv,
coverage_maps_path=self.coverage_maps,
coverage_maps_prefix=self.name,
excluded_projects=excluded_projects)
logger.info('Running cascade_report on %s.', self)
with open(self.g2p_summary_csv) as g2p_summary_csv, \
open(self.remap_counts_csv) as remap_counts_csv, \
open(self.aligned_csv) as aligned_csv, \
open(self.cascade_csv, 'w') as cascade_csv:
cascade_report = CascadeReport(cascade_csv)
cascade_report.g2p_summary_csv = g2p_summary_csv
cascade_report.remap_counts_csv = remap_counts_csv
cascade_report.aligned_csv = aligned_csv
cascade_report.generate()
logger.info('Finished sample %s.', self)
def process_sample(sample_index, run_info, args, pssm):
""" Process a single sample.
:param sample_index: which sample to process from the session JSON
:param run_info: run parameters loaded from the session JSON
:param args: the command-line arguments
:param pssm: the pssm library for running G2P analysis
"""
scratch_path = os.path.join(args.data_path, 'scratch')
sample_info = run_info.samples[sample_index]
sample_id = sample_info['Id']
sample_name = sample_info['Name']
sample_dir = os.path.join(args.data_path, 'input', 'samples', sample_id,
'Data', 'Intensities', 'BaseCalls')
if not os.path.exists(sample_dir):
sample_dir = os.path.join(args.data_path, 'input', 'samples',
sample_id)
sample_path = None
for root, _dirs, files in os.walk(sample_dir):
sample_paths = fnmatch.filter(files, '*_R1_*')
if sample_paths:
sample_path = os.path.join(root, sample_paths[0])
break
if sample_path is None:
raise RuntimeError(
'No R1 file found for sample id {}.'.format(sample_id))
sample_path2 = sample_path.replace('_R1_', '_R2_')
if not os.path.exists(sample_path2):
raise RuntimeError('R2 file missing for sample id {}: {!r}.'.format(
sample_id, sample_path2))
logger.info('Processing sample %s (%d of %d): %s (%s).', sample_id,
sample_index + 1, len(run_info.samples), sample_name,
sample_path)
sample_qc_path = os.path.join(args.qc_path, sample_name)
makedirs(sample_qc_path)
sample_scratch_path = os.path.join(scratch_path, sample_name)
makedirs(sample_scratch_path)
bad_cycles_path = os.path.join(scratch_path, 'bad_cycles.csv')
trimmed_path1 = os.path.join(sample_scratch_path, 'trimmed1.fastq')
read_summary_path = os.path.join(sample_scratch_path, 'read_summary.csv')
trimmed_path2 = os.path.join(sample_scratch_path, 'trimmed2.fastq')
with open(read_summary_path, 'w') as read_summary:
trim((sample_path, sample_path2),
bad_cycles_path, (trimmed_path1, trimmed_path2),
summary_file=read_summary,
use_gzip=sample_path.endswith('.gz'))
logger.info('Running fastq_g2p (%d of %d).', sample_index + 1,
len(run_info.samples))
g2p_unmapped1_path = os.path.join(sample_scratch_path,
'g2p_unmapped1.fastq')
g2p_unmapped2_path = os.path.join(sample_scratch_path,
'g2p_unmapped2.fastq')
with open(os.path.join(sample_scratch_path, 'trimmed1.fastq'), 'r') as fastq1, \
open(os.path.join(sample_scratch_path, 'trimmed2.fastq'), 'r') as fastq2, \
open(os.path.join(sample_scratch_path, 'g2p.csv'), 'w') as g2p_csv, \
open(os.path.join(sample_scratch_path, 'g2p_summary.csv'), 'w') as g2p_summary_csv, \
open(g2p_unmapped1_path, 'w') as g2p_unmapped1, \
open(g2p_unmapped2_path, 'w') as g2p_unmapped2, \
open(os.path.join(sample_scratch_path, 'g2p_aligned.csv'), 'w') as g2p_aligned_csv:
fastq_g2p(pssm=pssm,
fastq1=fastq1,
fastq2=fastq2,
g2p_csv=g2p_csv,
g2p_summary_csv=g2p_summary_csv,
unmapped1=g2p_unmapped1,
unmapped2=g2p_unmapped2,
aligned_csv=g2p_aligned_csv,
min_count=DEFAULT_MIN_COUNT,
min_valid=MIN_VALID,
min_valid_percent=MIN_VALID_PERCENT)
logger.info('Running prelim_map (%d of %d).', sample_index + 1,
len(run_info.samples))
excluded_seeds = [] if args.all_projects else EXCLUDED_SEEDS
with open(os.path.join(sample_scratch_path, 'prelim.csv'),
'w') as prelim_csv:
prelim_map(g2p_unmapped1_path,
g2p_unmapped2_path,
prelim_csv,
work_path=sample_scratch_path,
excluded_seeds=excluded_seeds)
logger.info('Running remap (%d of %d).', sample_index + 1,
len(run_info.samples))
if args.debug_remap:
debug_file_prefix = os.path.join(sample_scratch_path, 'debug')
else:
debug_file_prefix = None
with open(os.path.join(sample_scratch_path, 'prelim.csv'), 'r') as prelim_csv, \
open(os.path.join(sample_scratch_path, 'remap.csv'), 'w') as remap_csv, \
open(os.path.join(sample_scratch_path, 'remap_counts.csv'), 'w') as counts_csv, \
open(os.path.join(sample_scratch_path, 'remap_conseq.csv'), 'w') as conseq_csv, \
open(os.path.join(sample_qc_path, 'unmapped1.fastq'), 'w') as unmapped1, \
open(os.path.join(sample_qc_path, 'unmapped2.fastq'), 'w') as unmapped2:
remap(g2p_unmapped1_path,
g2p_unmapped2_path,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
sample_scratch_path,
debug_file_prefix=debug_file_prefix)
logger.info('Running sam2aln (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'remap.csv'), 'r') as remap_csv, \
open(os.path.join(sample_scratch_path, 'aligned.csv'), 'w') as aligned_csv, \
open(os.path.join(sample_scratch_path, 'conseq_ins.csv'), 'w') as conseq_ins_csv, \
open(os.path.join(sample_scratch_path, 'failed_read.csv'), 'w') as failed_csv, \
open(os.path.join(sample_scratch_path, 'clipping.csv'), 'w') as clipping_csv:
sam2aln(remap_csv,
aligned_csv,
conseq_ins_csv,
failed_csv,
clipping_csv=clipping_csv)
logger.info('Running aln2counts (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'aligned.csv'), 'r') as aligned_csv, \
open(os.path.join(sample_scratch_path, 'g2p_aligned.csv'), 'r') as g2p_aligned_csv, \
open(os.path.join(sample_scratch_path, 'clipping.csv'), 'r') as clipping_csv, \
open(os.path.join(sample_scratch_path, 'conseq_ins.csv'), 'r') as conseq_ins_csv, \
open(os.path.join(sample_scratch_path, 'remap_conseq.csv'), 'r') as remap_conseq_csv, \
open(os.path.join(sample_scratch_path, 'nuc.csv'), 'w') as nuc_csv, \
open(os.path.join(sample_scratch_path, 'amino.csv'), 'w') as amino_csv, \
open(os.path.join(sample_scratch_path, 'coord_ins.csv'), 'w') as coord_ins_csv, \
open(os.path.join(sample_scratch_path, 'conseq.csv'), 'w') as conseq_csv, \
open(os.path.join(sample_scratch_path, 'failed_align.csv'), 'w') as failed_align_csv, \
open(os.path.join(sample_scratch_path, 'coverage_summary.csv'), 'w') as coverage_summary_csv:
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
coverage_summary_csv=coverage_summary_csv,
clipping_csv=clipping_csv,
conseq_ins_csv=conseq_ins_csv,
g2p_aligned_csv=g2p_aligned_csv,
remap_conseq_csv=remap_conseq_csv)
logger.info('Running coverage_plots (%d of %d).', sample_index + 1,
len(run_info.samples))
coverage_maps_path = os.path.join(args.qc_path, 'coverage_maps')
makedirs(coverage_maps_path)
excluded_projects = [] if args.all_projects else EXCLUDED_PROJECTS
with open(os.path.join(sample_scratch_path, 'amino.csv'), 'r') as amino_csv, \
open(os.path.join(sample_scratch_path, 'coverage_scores.csv'), 'w') as coverage_scores_csv:
coverage_plot(amino_csv,
coverage_scores_csv,
coverage_maps_path=coverage_maps_path,
coverage_maps_prefix=sample_name,
excluded_projects=excluded_projects)
logger.info('Running hivdb (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'amino.csv')) as amino_csv, \
open(os.path.join(sample_scratch_path, 'coverage_scores.csv')) as coverage_scores_csv, \
open(os.path.join(sample_scratch_path, 'resistance.csv'), 'w') as resistance_csv, \
open(os.path.join(sample_scratch_path, 'mutations.csv'), 'w') as mutations_csv:
hivdb(amino_csv, coverage_scores_csv, resistance_csv, mutations_csv,
run_info.reports)
logger.info('Running resistance report (%d of %d).', sample_index + 1,
len(run_info.samples))
source_path = os.path.dirname(__file__)
version_filename = os.path.join(source_path, 'version.txt')
if not os.path.exists(version_filename):
git_version = 'v0-dev'
else:
with open(version_filename) as version_file:
git_version = version_file.read().strip()
reports_path = os.path.join(args.qc_path, 'resistance_reports')
makedirs(reports_path)
report_filename = os.path.join(reports_path,
sample_name + '_resistance.pdf')
with open(os.path.join(sample_scratch_path, 'resistance.csv')) as resistance_csv, \
open(os.path.join(sample_scratch_path, 'mutations.csv')) as mutations_csv, \
open(report_filename, 'wb') as report_pdf:
gen_report(resistance_csv,
mutations_csv,
report_pdf,
sample_name,
git_version=git_version)
logger.info('Running cascade_report (%d of %d).', sample_index + 1,
len(run_info.samples))
with open(os.path.join(sample_scratch_path, 'g2p_summary.csv'), 'r') as g2p_summary_csv, \
open(os.path.join(sample_scratch_path, 'remap_counts.csv'), 'r') as remap_counts_csv, \
open(os.path.join(sample_scratch_path, 'aligned.csv'), 'r') as aligned_csv, \
open(os.path.join(sample_scratch_path, 'cascade.csv'), 'w') as cascade_csv:
cascade_report = CascadeReport(cascade_csv)
cascade_report.g2p_summary_csv = g2p_summary_csv
cascade_report.remap_counts_csv = remap_counts_csv
cascade_report.aligned_csv = aligned_csv
cascade_report.generate()
logger.info('Finished sample (%d of %d).', sample_index + 1,
len(run_info.samples))
def process(self,
pssm,
excluded_seeds=(),
excluded_projects=(),
force_gzip=False):
""" Process a single sample.
:param pssm: the pssm library for running G2P analysis
:param excluded_seeds: seeds to exclude from mapping
:param excluded_projects: project codes to exclude from reporting
:param bool force_gzip: treat FASTQ files as gzipped, even when they
don't end in .gz
"""
logger.info('Processing %s (%r).', self, self.fastq1)
scratch_path = os.path.dirname(self.prelim_csv)
os.mkdir(scratch_path)
use_gzip = force_gzip or self.fastq1.endswith('.gz')
with open(self.read_summary_csv, 'w') as read_summary:
trim((self.fastq1, self.fastq2),
self.bad_cycles_csv,
(self.trimmed1_fastq, self.trimmed2_fastq),
summary_file=read_summary,
use_gzip=use_gzip)
logger.info('Running fastq_g2p on %s.', self)
with open(self.trimmed1_fastq) as fastq1, \
open(self.trimmed2_fastq) as fastq2, \
open(self.g2p_csv, 'w') as g2p_csv, \
open(self.g2p_summary_csv, 'w') as g2p_summary_csv, \
open(self.g2p_unmapped1_fastq, 'w') as g2p_unmapped1, \
open(self.g2p_unmapped2_fastq, 'w') as g2p_unmapped2, \
open(self.g2p_aligned_csv, 'w') as g2p_aligned_csv:
fastq_g2p(pssm=pssm,
fastq1=fastq1,
fastq2=fastq2,
g2p_csv=g2p_csv,
g2p_summary_csv=g2p_summary_csv,
unmapped1=g2p_unmapped1,
unmapped2=g2p_unmapped2,
aligned_csv=g2p_aligned_csv,
min_count=DEFAULT_MIN_COUNT,
min_valid=MIN_VALID,
min_valid_percent=MIN_VALID_PERCENT)
logger.info('Running prelim_map on %s.', self)
with open(self.prelim_csv, 'w') as prelim_csv:
prelim_map(self.g2p_unmapped1_fastq,
self.g2p_unmapped2_fastq,
prelim_csv,
work_path=scratch_path,
excluded_seeds=excluded_seeds)
logger.info('Running remap on %s.', self)
if self.debug_remap:
debug_file_prefix = os.path.join(scratch_path, 'debug')
else:
debug_file_prefix = None
with open(self.prelim_csv) as prelim_csv, \
open(self.remap_csv, 'w') as remap_csv, \
open(self.remap_counts_csv, 'w') as counts_csv, \
open(self.remap_conseq_csv, 'w') as conseq_csv, \
open(self.unmapped1_fastq, 'w') as unmapped1, \
open(self.unmapped2_fastq, 'w') as unmapped2:
remap(self.g2p_unmapped1_fastq,
self.g2p_unmapped2_fastq,
prelim_csv,
remap_csv,
counts_csv,
conseq_csv,
unmapped1,
unmapped2,
scratch_path,
debug_file_prefix=debug_file_prefix)
logger.info('Running sam2aln on %s.', self)
with open(self.remap_csv) as remap_csv, \
open(self.aligned_csv, 'w') as aligned_csv, \
open(self.conseq_ins_csv, 'w') as conseq_ins_csv, \
open(self.failed_csv, 'w') as failed_csv, \
open(self.clipping_csv, 'w') as clipping_csv:
sam2aln(remap_csv,
aligned_csv,
conseq_ins_csv,
failed_csv,
clipping_csv=clipping_csv)
logger.info('Running aln2counts on %s.', self)
with open(self.aligned_csv) as aligned_csv, \
open(self.g2p_aligned_csv) as g2p_aligned_csv, \
open(self.clipping_csv) as clipping_csv, \
open(self.conseq_ins_csv) as conseq_ins_csv, \
open(self.remap_conseq_csv) as remap_conseq_csv, \
open(self.nuc_csv, 'w') as nuc_csv, \
open(self.amino_csv, 'w') as amino_csv, \
open(self.coord_ins_csv, 'w') as coord_ins_csv, \
open(self.conseq_csv, 'w') as conseq_csv, \
open(self.conseq_region_csv, 'w') as conseq_region_csv, \
open(self.failed_align_csv, 'w') as failed_align_csv, \
open(self.coverage_summary_csv, 'w') as coverage_summary_csv:
aln2counts(aligned_csv,
nuc_csv,
amino_csv,
coord_ins_csv,
conseq_csv,
failed_align_csv,
coverage_summary_csv=coverage_summary_csv,
clipping_csv=clipping_csv,
conseq_ins_csv=conseq_ins_csv,
g2p_aligned_csv=g2p_aligned_csv,
remap_conseq_csv=remap_conseq_csv,
conseq_region_csv=conseq_region_csv)
logger.info('Running coverage_plots on %s.', self)
os.makedirs(self.coverage_maps)
with open(self.amino_csv) as amino_csv, \
open(self.coverage_scores_csv, 'w') as coverage_scores_csv:
coverage_plot(amino_csv,
coverage_scores_csv,
coverage_maps_path=self.coverage_maps,
coverage_maps_prefix=self.name,
excluded_projects=excluded_projects)
logger.info('Running cascade_report on %s.', self)
with open(self.g2p_summary_csv) as g2p_summary_csv, \
open(self.remap_counts_csv) as remap_counts_csv, \
open(self.aligned_csv) as aligned_csv, \
open(self.cascade_csv, 'w') as cascade_csv:
cascade_report = CascadeReport(cascade_csv)
cascade_report.g2p_summary_csv = g2p_summary_csv
cascade_report.remap_counts_csv = remap_counts_csv
cascade_report.aligned_csv = aligned_csv
cascade_report.generate()
logger.info('Finished sample %s.', self)